results

Published

January 28, 2025

Introduction

Effects of wildfire smoke on daily respiratory acute-care utilization has been estimated at the zip-code level in California (Do et al).

Rate differences per 100,000 are estimated at the zip code level.

Effect modification of this effect by community characteristics has been estimated.

The increase in risk difference per IQR increase in air conditioning prevalence is -0.239302618 (95% CI, -0.41143431, -0.0671709235), the corresponding IQR for AC prevalence is 6.000915e-01.

Over-arching question: How would hypothetically changing the distribution of the effect modifiers affect the number and distribution of respiratory acute-care utilization?

Baseline risk difference (zcta-level)

Histogram and summary statistics

RD is per 100,000

    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-19.8663  -0.8485  -0.0663   0.1074   0.8228  29.6049 

Map

Air conditioning (zcta-level)

Distribution of air conditioning at the zip-code level

Histogram and summary statistics

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0000  0.2609  0.6250  0.5666  0.8710  1.0000     227 

Map

Methods

Scenario definitions:

Hypothetically change the distribution of AC in three ways to assess how it would change the risk difference at the ZCTA level.

ac_25th ac_50th ac_75th ac_100th ac_iqr
0.2608696 0.625 0.8709678 1 0.6100982

Scenario 1: Among those zip codes with an AC prevalence below the median, raise their AC prevalence to the median (0.6250).

Scenario 2: Among those zip codes with an AC prevalence below the 75th percentile, raise their AC prevalence to the 75th percentile (0.8710).

Scenario 3: Same, but 100th percentile (1).

HIA method

For each zip code, calculate the change in the AC proportion from the status quo to the target level. For example, if a zip code has a 50% AC prevalence, the difference from baseline to target in Scenario 1 is 12.5% (65.5%-50%). Then, express that difference in terms of the number of IQRs that it represents. The IQR of AC is 0.61 (above). That zip code would raise its AC prevalence by

0.125/.61
[1] 0.204918

Then, use that value to calculate the new risk difference under that scenario, following this equation:

rd_target_pt=rd_baseline_pt+rd_per_ac_iqr_pt*ac_prop_change_per_iqr

where * rd_target_pt = the zip code’s new risk difference under the scenario

  • rd_per_ac_iqr_pt = the increase in the risk difference per change in IQR of AC

  • ac_prop_change_per_iqr = the number of IQRs changed in that zip code in that scenario

This assumes that the increase is linear.

Uncertainty

Calculate uncertainty by re-sampling rd_baseline_pt and rd_per_ac_iqr_pt from a normal distribution with 1,000 bootstrap replications. Uncertainty interval is 2.5th and 97.5th percentiles.

Results

Overall summary

There are 1,396 total zip codes

n_zcta_intervene is the number of zip codes whose AC values would be changed under the scenario.

Risk differences are expressed per 100,000.

Means and sums are for those zctas with intervention

target_percentile n_zcta_intervene rd_baseline_sum rd_baseline_sum_ll rd_baseline_sum_ul rd_baseline_mean_unwt rd_baseline_mean_unwt_ll rd_baseline_mean_unwt_ul rd_target_sum rd_target_sum_ll rd_target_sum_ul rd_target_mean_unwt rd_target_mean_unwt_ll rd_target_mean_unwt_ul rd_target_v_baseline_diff_in_sum rd_target_v_baseline_diff_in_sum_ll rd_target_v_baseline_diff_in_sum_ul
0.50 580 129.604 51.876 208.044 0.223 0.089 0.359 49.258 82.215 236.335 0.085 0.142 0.407 80.345 -32.392 -26.742
0.75 876 144.325 53.005 246.992 0.165 0.061 0.282 -5.982 109.718 300.287 -0.007 0.125 0.343 150.307 -59.610 -50.506
1.00 1013 121.820 21.253 223.076 0.120 0.021 0.220 -76.922 92.033 296.173 -0.076 0.091 0.292 198.743 -78.332 -67.459

Maps of results for each scenario

Target percentile: 0.5

Target percentile: 0.75

Target percentile: 1